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@ARTICLE{Labhart_PLOSONE_2021,
         author = {Labhart, Florian and Muralidhar, Skanda and Mass{\'{e}}, Benoit and Meegahapola, Lakmal Buddika and Kuntsche, Emmanuel and Gatica-Perez, Daniel},
       keywords = {alcohol use, computer algorithm, drinking context, Ecological momentary assessment, Smartphone application, Video annotation},
       projects = {Idiap, DUSK2DAWN, Youth@Night},
          title = {Ten seconds of my nights: exploring methods to measure brightness, loudness and attendance and their associations with alcohol use from video clips},
        journal = {PLOS ONE},
           year = {2021},
            doi = {https://doi.org/10.1371/journal.pone.0250443},
       abstract = {Introduction: Most evidence on associations between alcohol use behaviors and the characteristics of its social and physical context is based on self-reports from study participants and, thus, only account for their subjective impressions of the situation. This study explores the feasibility of obtaining alternative measures of loudness, brightness, and attendance (number of people) using 10-second video clips of real-life drinking occasions rated by human annotators and computer algorithms, and explores the associations of these measures with participants choice to drink alcohol or not.

Methods: Using a custom-built smartphone application, 215 16-25-year-olds documented characteristics of 2,380 weekend night drinking events using questionnaires and videos. Ratings of loudness, brightness, and attendance were obtained from three sources, namely in-situ participants ratings, video-based annotator ratings, and video-based computer algorithm ratings. Bivariate statistics explored differences in ratings across sources. Multilevel logistic regressions assessed the associations of contextual characteristics with alcohol use. Finally, model fit indices and cross-validation were used to assess the ability of each set of contextual measures to predict participants alcohol use.

Results: Raw ratings of brightness, loudness and attendance differed slightly across sources, but were all correlated (r = .21 to .82, all p smaller than .001). Participants rated bars/pubs as being louder (Cohen's d = 0.50 [95\%-CI: 0.07-0.92]), and annotators rated private places as darker (d = 1.21 [95\%-CI: 0.99-1.43]) when alcohol was consumed than when alcohol was not consumed. Multilevel logistic regressions showed that drinking in private places was more likely in louder (ORparticipants = 1.74 [CI: 1.31-2.32]; ORannotators = 3.22 [CI: 2.06-5.03]; ORalgorithm = 2.62 [CI: 1.83-3.76]), more attended (ORparticipants = 1.10 [CI: 1.03-1.18]; ORalgorithm = 1.19 [CI: 1.07-1.32]) and darker (OR = 0.64 [CI: 0.44-0.94]) situations. In commercial venues, drinking was more likely in darker (ORparticipants = 0.67 [CI: 0.47-0.94]; ORannotators = 0.53 [CI: 0.33-0.85]; ORalgorithm = 0.58 [CI: 0.37-0.88]) and louder (ORparticipants = 1.40 [CI: 1.02-1.92]; ORalgorithm = 2.45 [CI: 1.25-4.80]) places. Higher inference accuracies were found for the models based on the annotators ratings (80\% to 84\%) and the algorithms ratings (76\% to 86\%) than on the participants ratings (69\% to 71\%).

Conclusions: Several contextual characteristics are associated with increased odds of drinking in private and commercial settings, and might serve as a basis for the development of prevention measures. Regarding assessment of contextual characteristics, annotators and algorithms might serve as appropriate substitutes of participants in-situ impressions for correlational and regression analyses despite differences in raw ratings. Collecting contextual data by means of sensors or media files is recommended for future research.},
            pdf = {https://publications.idiap.ch/attachments/papers/2021/Labhart_PLOSONE_2021.pdf}
}